DEPR: Enforce rolling.count min_periods deprecation

doc/source/whatsnew/v2.0.0.rst

@@ -146,6 +146,7 @@ Removal of prior version deprecations/changes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 - Disallow passing non-round floats to :class:`Timestamp` with ``unit="M"`` or ``unit="Y"`` (:issue:`47266`)
 - Remove :meth:`DataFrameGroupBy.pad` and :meth:`DataFrameGroupBy.backfill` (:issue:`45076`)
+- Enforced :meth:`Rolling.count` with ``min_periods=None`` to default to the size of the window (:issue:`31302`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_200.performance:

pandas/core/window/rolling.py

@@ -1936,21 +1936,21 @@ def aggregate(self, func, *args, **kwargs):
             """
         >>> s = pd.Series([2, 3, np.nan, 10])
         >>> s.rolling(2).count()
-        0    1.0
+        0    NaN
         1    2.0
         2    1.0
         3    1.0
         dtype: float64
         >>> s.rolling(3).count()
-        0    1.0
-        1    2.0
+        0    NaN
+        1    NaN
         2    2.0
         3    2.0
         dtype: float64
         >>> s.rolling(4).count()
-        0    1.0
-        1    2.0
-        2    2.0
+        0    NaN
+        1    NaN
+        2    NaN
         3    3.0
         dtype: float64
         """
@@ -1960,22 +1960,7 @@ def aggregate(self, func, *args, **kwargs):
         agg_method="count",
     )
     def count(self, numeric_only: bool = False):
-        if self.min_periods is None:
-            warnings.warn(
-                (
-                    "min_periods=None will default to the size of window "
-                    "consistent with other methods in a future version. "
-                    "Specify min_periods=0 instead."
-                ),
-                FutureWarning,
-                stacklevel=find_stack_level(),
-            )
-            self.min_periods = 0
-            result = super().count()
-            self.min_periods = None
-        else:
-            result = super().count(numeric_only)
-        return result
+        return super().count(numeric_only)
 
     @doc(
         template_header,

pandas/tests/window/test_base_indexer.py

@@ -147,7 +147,6 @@ def get_window_bounds(self, num_values, min_periods, center, closed, step):
         ),
     ],
 )
-@pytest.mark.filterwarnings("ignore:min_periods:FutureWarning")
 def test_rolling_forward_window(
     frame_or_series, func, np_func, expected, np_kwargs, step
 ):

pandas/tests/window/test_groupby.py

@@ -96,10 +96,7 @@ def test_getitem_multiple(self, roll_frame):
             "mean",
             "min",
             "max",
-            pytest.param(
-                "count",
-                marks=pytest.mark.filterwarnings("ignore:min_periods:FutureWarning"),
-            ),
+            "count",
             "kurt",
             "skew",
         ],

pandas/tests/window/test_rolling.py

@@ -738,8 +738,7 @@ def test_rolling_count_default_min_periods_with_null_values(frame_or_series):
     expected_counts = [1.0, 2.0, 3.0, 2.0, 2.0, 2.0, 3.0]
 
     # GH 31302
-    with tm.assert_produces_warning(FutureWarning):
-        result = frame_or_series(values).rolling(3).count()
+    result = frame_or_series(values).rolling(3, min_periods=0).count()
     expected = frame_or_series(expected_counts)
     tm.assert_equal(result, expected)
 

pandas/tests/window/test_rolling_functions.py

@@ -23,12 +23,11 @@
     [
         [np.mean, "mean", {}],
         [np.nansum, "sum", {}],
-        pytest.param(
+        [
             lambda x: np.isfinite(x).astype(float).sum(),
             "count",
             {},
-            marks=pytest.mark.filterwarnings("ignore:min_periods:FutureWarning"),
-        ),
+        ],
         [np.median, "median", {}],
         [np.min, "min", {}],
         [np.max, "max", {}],
@@ -50,12 +49,11 @@ def test_series(series, compare_func, roll_func, kwargs, step):
     [
         [np.mean, "mean", {}],
         [np.nansum, "sum", {}],
-        pytest.param(
+        [
             lambda x: np.isfinite(x).astype(float).sum(),
             "count",
             {},
-            marks=pytest.mark.filterwarnings("ignore:min_periods:FutureWarning"),
-        ),
+        ],
         [np.median, "median", {}],
         [np.min, "min", {}],
         [np.max, "max", {}],

pandas/tests/window/test_timeseries_window.py

@@ -592,10 +592,7 @@ def test_freqs_ops(self, freq, op, result_data):
         [
             "sum",
             "mean",
-            pytest.param(
-                "count",
-                marks=pytest.mark.filterwarnings("ignore:min_periods:FutureWarning"),
-            ),
+            "count",
             "median",
             "std",
             "var",